Source code for jmetal.problem.multiobjective.constrained

from math import pi, cos, atan

from jmetal.core.problem import FloatProblem
from jmetal.core.solution import FloatSolution

"""
.. module:: constrained
   :platform: Unix, Windows
   :synopsis: Constrained test problems for multi-objective optimization

.. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es>
"""


[docs]class Srinivas(FloatProblem): """ Class representing problem Srinivas. """ def __init__(self): super(Srinivas, self).__init__() self.number_of_variables = 2 self.number_of_objectives = 2 self.number_of_constraints = 2 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [-20.0 for _ in range(self.number_of_variables)] self.upper_bound = [20.0 for _ in range(self.number_of_variables)]
[docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: x1 = solution.variables[0] x2 = solution.variables[1] solution.objectives[0] = 2.0 + (x1 - 2.0) * (x1 - 2.0) + (x2 - 1.0) * (x2 - 1.0) solution.objectives[1] = 9.0 * x1 - (x2 - 1.0) * (x2 - 1.0) self.__evaluate_constraints(solution) return solution
def __evaluate_constraints(self, solution: FloatSolution) -> None: x1 = solution.variables[0] x2 = solution.variables[1] solution.constraints[0] = 1.0 - (x1 * x1 + x2 * x2) / 225.0 solution.constraints[1] = (3.0 * x2 - x1) / 10.0 - 1.0
[docs] def get_name(self): return 'Srinivas'
[docs]class Tanaka(FloatProblem): """ Class representing problem Tanaka. """ def __init__(self): super(Tanaka, self).__init__() self.number_of_variables = 2 self.number_of_objectives = 2 self.number_of_constraints = 2 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [10e-5 for _ in range(self.number_of_variables)] self.upper_bound = [pi for _ in range(self.number_of_variables)]
[docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: solution.objectives[0] = solution.variables[0] solution.objectives[1] = solution.variables[1] self.__evaluate_constraints(solution) return solution
def __evaluate_constraints(self, solution: FloatSolution) -> None: constraints = [0.0 for _ in range(self.number_of_constraints)] x1 = solution.variables[0] x2 = solution.variables[1] constraints[0] = (x1 * x1 + x2 * x2 - 1.0 - 0.1 * cos(16.0 * atan(x1 / x2))) constraints[1] = -2.0 * ((x1 - 0.5) * (x1 - 0.5) + (x2 - 0.5) * (x2 - 0.5) - 0.5) solution.constraints = constraints #set_overall_constraint_violation_degree(solution)
[docs] def get_name(self): return 'Tanaka'
[docs]class Osyczka2(FloatProblem): """ Class representing problem Osyczka2. """ def __init__(self): super(Osyczka2, self).__init__() self.number_of_variables = 6 self.number_of_objectives = 2 self.number_of_constraints = 6 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [0.0, 0.0, 1.0, 0.0, 1.0, 0.0] self.upper_bound = [10.0, 10.0, 5.0, 6.0, 5.0, 10.0] FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound
[docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: x = solution.variables solution.objectives[0] = - (25.0 * (x[0] - 2.0) ** 2 + (x[1] - 2.0) ** 2 + (x[2] - 1.0) ** 2 + (x[3] - 4.0) ** 2 + (x[4] - 1.0) ** 2) solution.objectives[1] = sum([x[i] ** 2 for i in range(len(x))]) self.__evaluate_constraints(solution) return solution
def __evaluate_constraints(self, solution: FloatSolution) -> None: constraints = [0.0 for _ in range(self.number_of_constraints)] x = solution.variables constraints[0] = (x[0] + x[1]) / 2.0 - 1.0 constraints[1] = (6.0 - x[0] - x[1]) / 6.0 constraints[2] = (2.0 - x[1] + x[0]) / 2.0 constraints[3] = (2.0 - x[0] + 3.0 * x[1]) / 2.0 constraints[4] = (4.0 - (x[2] - 3.0) * (x[2] - 3.0) - x[3]) / 4.0 constraints[5] = ((x[4] - 3.0) * (x[4] - 3.0) + x[5] - 4.0) / 4.0 solution.constraints = constraints
[docs] def get_name(self): return 'Osyczka2'
[docs]class Binh2(FloatProblem): """ Class representing problem Binh2. """ def __init__(self): super(Binh2, self).__init__() self.number_of_variables = 2 self.number_of_objectives = 2 self.number_of_constraints = 2 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [0.0, 0.0] self.upper_bound = [5.0, 3.0] FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound
[docs] def evaluate(self, solution: FloatSolution) -> FloatSolution: x = solution.variables solution.objectives[0] = 4.0 * x[0] * x[0] + 4 * x[1] * x[1] solution.objectives[1] = (x[0] - 5.0) * (x[0] - 5.0) + (x[1] - 5.0) * (x[1] - 5.0) self.__evaluate_constraints(solution) return solution
def __evaluate_constraints(self, solution: FloatSolution) -> None: constraints = [0.0 for _ in range(self.number_of_constraints)] x = solution.variables constraints[0] = -1.0 * (x[0] - 5) * (x[0] - 5) - x[1] * x[1] + 25.0 constraints[1] = (x[0] - 8) * (x[0] - 8) + (x[1] + 3) * (x[1] + 3) - 7.7
[docs] def get_name(self): return 'Binh2'